Computational Discovery of Cost-effective Materials for Carbon Capture and Other Molecular Separations

Carbon Emissions Problem

World carbon emissions continue to grow, and in 2011 they reached a record high of 35 billion tons. Power plants are responsible for about 40% of the total.

Carbon dioxide is a greenhouse gas and major contributor to climate change.

Carbon Capture Solution

The most promising carbon capture technology is adsorption, which involves a column packed with a porous material that selectively soaks up carbon dioxide over nitrogen. The typical choice of material is a zeolite, which separates molecules based on shape and size like a "molecular sieve." It is critical to choose the best zeolite to achieve optimal performance of the process.

We are the only group in the world with a computational screening approach that combines material selection and process optimization for the discovery of minimum-cost zeolites for adsorption-based separation processes.

Impact of Innovation

Using our novel computational screening approach, we have discovered a zeolite that is predicted to reduce the costs of carbon capture and compression using an adsorption process by 25% compared to the previous best.

Markets and Applications

Our computational framework is general and can be applied to any molecular separation. It is particularly valuable for high impact chemicals that are difficult to separate through traditional means, typically expensive cryogenic distillation.

Zeolites are already used extensively, and the market for synthetic zeolites is $2 billion. Combined, the markets for the separations below total $500 billion. Even a small improvement in separation technology can have a huge impact.

Toward the Future

We have obtained provisional patent protection (applications #61/761,436, #61/765,284, and #61/873,940) and are in the process of experimental validation.

We envision establishing a "sorbent replacement business" where we tailor our method to a client's specific process to design more cost-effective separations.

This work was partially supported by the National Science Foundation under awards EFRI-0937706 and CBET-1263165. E.L.F. is also thankful for his National Defense Science and Engineering Graduate (NDSEG) fellowship. A portion of the computation was performed at the TIGRESS high performance computer center at Princeton University, which is jointly supported by the Princeton Institute for Computational Science and Engineering and the Princeton University Office of Information Technology.